# /usr/bin/env python
# coding=utf-8
import os
from gen_captcha import gen_captcha_text_and_image, MAX_CAPTCHA
from constants import number, alphabet, ALPHABET, MATH_STRINGS, CHAR_2_POS_DICT, POS_2_CHAR
from gen_captcha import modelNo
import numpy as np
import tensorflow as tf

IMAGE_HEIGHT = 60
IMAGE_WIDTH = 160


# 把彩色图像转为灰度图像（色彩对识别验证码没有什么用）
def convert2gray(img):
    if len(img.shape) > 2:
        gray = np.mean(img, -1)
        return gray
    else:
        return img


char_set = number + alphabet + ALPHABET + MATH_STRINGS
CHAR_SET_LEN = len(char_set)


def text2vec(text):
    text_len = len(text)
    if text_len > MAX_CAPTCHA:
        raise ValueError('验证码最长5个字符')

    vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)

    def char2pos(c):
        k = CHAR_2_POS_DICT[c]
        return k

    for i, c in enumerate(text):
        idx = i * CHAR_SET_LEN + int(char2pos(c))
        vector[idx] = 1
    return vector


def vec2text(vec):
    char_pos = vec.nonzero()[0]
    text = []
    for i, c in enumerate(char_pos):
        char_idx = c % CHAR_SET_LEN
        char_code = POS_2_CHAR[str(char_idx)]
        text.append(char_code)
    return ''.join(text)


# 生成一个训练batch
def get_next_batch(batch_size=128):
    batch_x = np.zeros([batch_size, IMAGE_HEIGHT * IMAGE_WIDTH])
    batch_y = np.zeros([batch_size, MAX_CAPTCHA * CHAR_SET_LEN])

    # 有时生成图像大小不是(60, 160, 3)
    def wrap_gen_captcha_text_and_image():
        while True:
            text, image = gen_captcha_text_and_image()
            if image.shape == (60, 160, 3):
                return text, image

    for i in range(batch_size):
        text, image = wrap_gen_captcha_text_and_image()
        image = convert2gray(image)

        batch_x[i, :] = image.flatten() / 255  # (image.flatten()-128)/128  mean为0
        batch_y[i, :] = text2vec(text)

    return batch_x, batch_y


####################################################################

X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT * IMAGE_WIDTH])
Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA * CHAR_SET_LEN])
keep_prob = tf.placeholder(tf.float32)  # dropout


# 定义CNN
def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):
    x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])

    # w_c1_alpha = np.sqrt(2.0/(IMAGE_HEIGHT*IMAGE_WIDTH)) #
    # w_c2_alpha = np.sqrt(2.0/(3*3*32))
    # w_c3_alpha = np.sqrt(2.0/(3*3*64))
    # w_d1_alpha = np.sqrt(2.0/(8*32*64))
    # out_alpha = np.sqrt(2.0/1024)

    # 3 conv layer
    w_c1 = tf.Variable(w_alpha * tf.random_normal([3, 3, 1, 32]))
    b_c1 = tf.Variable(b_alpha * tf.random_normal([32]))
    conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))
    conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    conv1 = tf.nn.dropout(conv1, keep_prob)

    w_c2 = tf.Variable(w_alpha * tf.random_normal([3, 3, 32, 64]))
    b_c2 = tf.Variable(b_alpha * tf.random_normal([64]))
    conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=[1, 1, 1, 1], padding='SAME'), b_c2))
    conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    conv2 = tf.nn.dropout(conv2, keep_prob)

    w_c3 = tf.Variable(w_alpha * tf.random_normal([3, 3, 64, 64]))
    b_c3 = tf.Variable(b_alpha * tf.random_normal([64]))
    conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, w_c3, strides=[1, 1, 1, 1], padding='SAME'), b_c3))
    conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    conv3 = tf.nn.dropout(conv3, keep_prob)

    # Fully connected layer
    w_d = tf.Variable(w_alpha * tf.random_normal([8 * 32 * 40, 1024]))
    b_d = tf.Variable(b_alpha * tf.random_normal([1024]))
    dense = tf.reshape(conv3, [-1, w_d.get_shape().as_list()[0]])
    dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d))
    dense = tf.nn.dropout(dense, keep_prob)

    w_out = tf.Variable(w_alpha * tf.random_normal([1024, MAX_CAPTCHA * CHAR_SET_LEN]))
    b_out = tf.Variable(b_alpha * tf.random_normal([MAX_CAPTCHA * CHAR_SET_LEN]))
    out = tf.add(tf.matmul(dense, w_out), b_out)
    # out = tf.nn.softmax(out)
    return out


# 训练
def train_crack_captcha_cnn():
    # 查看gpu和cpu的数量
    # print("###########version###########", tf.__version__)
    # print("++++++++++is_gpu_available+++++++++++", tf.test.is_gpu_available()) #返回真才支持gpu
    # tensorflow-gpu==1.15(对于 1.15 及更早版本，CPU 和 GPU 软件包是分开的)
    import time
    start_time = time.time()
    output = crack_captcha_cnn()
    # loss
    # loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(output, Y))
    loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y))
    # 最后一层用来分类的softmax和sigmoid有什么不同？
    # optimizer 为了加快训练 learning_rate应该开始大，然后慢慢衰
    optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)

    predict = tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN])
    max_idx_p = tf.argmax(predict, 2)
    max_idx_l = tf.argmax(tf.reshape(Y, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
    correct_pred = tf.equal(max_idx_p, max_idx_l)
    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

    saver = tf.train.Saver()

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        # summary_writer = tf.summary.FileWriter('logs/test', sess.graph)
        step = 0
        while True:
            batch_x, batch_y = get_next_batch(100)
            _, loss_ = sess.run([optimizer, loss], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})
            print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())), step, loss_)

            # 每100 step计算一次准确率
            if step % 100 == 0:
                batch_x_test, batch_y_test = get_next_batch(100)
                acc = sess.run(accuracy, feed_dict={X: batch_x_test, Y: batch_y_test, keep_prob: 1.})
                print(u'***************************************************************第%s次的准确率为%s' % (step, acc))
                # 如果准确率大于50%,保存模型,完成训练
                if acc == 1:  ##我这里设了0.9，设得越大训练要花的时间越长，如果设得过于接近1，很难达到。如果使用cpu，花的时间很长，cpu占用很高电脑发烫。
                    saver.save(sess, "models/" + modelNo + "/" + modelNo + ".model", global_step=step)
                    print(time.time() - start_time)
                    break

            step += 1


train_crack_captcha_cnn()




# 以下为预测测试代码,修改模型号即可使用
# output = crack_captcha_cnn()
# saver = tf.train.Saver()
# sess = tf.Session()

# cdir = os.path.dirname(os.path.abspath(__file__))
# path = cdir+'/models/91/'
# if os.path.exists(path) == False:
#     print('缺少模型数据,path:'+path)
#     sys.exit(2)
# saver.restore(sess, tf.train.latest_checkpoint(path))

# while (1):

#     text, image = gen_captcha_text_and_image()
#     image = convert2gray(image)
#     image = image.flatten() / 255

#     predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
#     text_list = sess.run(predict, feed_dict={X: [image], keep_prob: 1})
#     predict_text = text_list[0].tolist()

#     # vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
#     # i = 0
#     # for t in predict_text:
#     #     vector[i * 63 + t] = 1
#     #     i += 1
#     #     # break
#     # 取映射关系的值,直接返回
#     res = ''
#     for t in predict_text:
#         res = res + char_set[t] #取数组下标的值

#     #print("正确: {}  预测: {}".format(text, vec2text(vector)))
#     print("正确: {}  预测: {}".format(text, res))

# 来源:https://www.cnblogs.com/ydf0509/p/6916435.html